Abstract
The mechanisms by which antimicrobials exert inhibitory effects against bacterial cells and by which bacteria display resistance vary under different conditions. Our understanding of the full complement of genes which can influence sensitivity to many antimicrobials is limited and often informed by experiments completed in a small set of exposure conditions. Capturing a broader suite of genes which contribute to survival under antimicrobial stress will improve our understanding of how antimicrobials work and how resistance can evolve. Here, we apply a new version of ‘TraDIS’ (Transposon Directed Insert Sequencing); a massively parallel transposon mutagenesis approach to identify different responses to the common biocide triclosan across a 125-fold range of concentrations. We have developed a new bioinformatic tool ‘AlbaTraDIS’ allowing both predictions of the impacts of individual transposon inserts on gene function to be made and comparisons across multiple TraDIS data sets. This new TraDIS approach allows essential genes as well as non-essential genes to be assayed for their contribution to bacterial survival and growth by modulating their expression. Our results demonstrate that different sets of genes are involved in survival following exposure to triclosan under a wide range of concentrations spanning bacteriostatic to bactericidal. The identified genes include those previously reported to have a role in triclosan resistance as well as a new set of genes not previously implicated in triclosan sensitivity. Amongst these novel genes are those involved in barrier function, small molecule uptake and integrity of transcription and translation. These data provide new insights into potential routes of triclosan entry and bactericidal mechanisms of action. Our data also helps to put recent work which has demonstrated the ubiquitous nature of triclosan in people and the built environment into context in terms of how different triclosan exposures may influence evolution of bacteria. We anticipate the approach we show here that allows comparisons across multiple experimental conditions of TraDIS data will be a starting point for future work examining how different drug conditions impact bacterial survival mechanisms.
